Tootfinder

CosmoGLINT: Cosmological Generative Model for Line Intensity Mapping with Transformer
Kana Moriwaki, Rui Lan Jun, Ken Osato, Naoki Yoshida
https://arxiv.org/abs/2506.16843

CosmoGLINT: Cosmological Generative Model for Line Intensity Mapping with Transformer
Modelling star-forming galaxies is crucial for upcoming observations of large-scale matter and galaxy distributions with galaxy redshift surveys and line intensity mapping (LIM). We introduce CosmoGLINT (Cosmological Generative model for Line INtensity mapping with Transformer), a Transformer-based generative framework designed to create realistic galaxy populations from dark matter (DM)-only simulations. CosmoGLINT auto-regressively generates sequences of galaxy properties -- including star fo…

Microsoft unveils Mu, an on-device small language model used in Copilot PCs, mapping natural language queries in the Settings app to function calls (Paul Thurrott/Thurrott)
https://www.thurrott.com/windows/windows-11/322465…

The Settings Agent in Windows 11 Has Its Own AI Model
Microsoft today detailed Mu, its latest small language model (SML) for Copilot+ PCs, which maps NL queries to Settings function calls.

High-speed quantitative nanomechanical mapping by photothermal off-resonance atomic force microscopy
Hans Gunstheimer, Gotthold Fl\"aschner, Jonathan D. Adams, Hendrik H\"olscher, Bart W. Hoogenboom
https://arxiv.org/abs/2506.16226

High-speed quantitative nanomechanical mapping by photothermal off-resonance atomic force microscopy
Atomic force microscopy (AFM) is widely used to measure surface topography of solid, soft, and living matter at the nanoscale. Moreover, by mapping forces as a function of distance to the surface, AFM can provide a wealth of information beyond topography, with nanomechanical properties as a prime example. Here we present a method based on photothermal off-resonance tapping (PORT) to increase the speed of such force spectroscopy measurements by at least an order of magnitude, thereby enabling hi…

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Minbok Wi, Seungmin Baek, Seonyong Park, Mattan Erez, Jung Ho Ahn
https://arxiv.org/abs/2506.15918

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Decomposing DRAM address mappings into component-level functions is critical for understanding memory behavior and enabling precise RowHammer attacks, yet existing reverse-engineering methods fall short. We introduce novel timing-based techniques leveraging DRAM refresh intervals and consecutive access latencies to infer component-specific functions. Based on this, we present Sudoku, the first software-based tool to automatically decompose full DRAM address mappings into channel, rank, bank gro…

Hier eine Aufgabe für erfahrene #OSM Mapper: Radinfrastruktur mappen in Berlin, es gibt sogar Bezahlung dafür:
https://community.openstreetmap.org/t/

Leute gesucht für bezahltes Radinfrastruktur-Mapping (Remote)
Hallo! Nachdem wir kürzlich um Hilfe gesucht haben um Parkraum in Berlin zu erfassen, freuen wir uns den Zuschlag für ein weiteres Projekt zur Datenerfassung – diesmal geht es um Radinfrastruktur-Daten – in Berlin bekommen zu haben. Auch für diese Projekt suchen wir Hilfe aus der Community im Rahmen von bezahlten Mapping-Aufgaben: Radinfrastruktur in Berlin erfassen Bei diesem Projekt wird es darum gehen, entlang des Vorrangnetzes für den Radverkehr die Rad- und Straßen-Infrastrukt…

Heterotopic energy for Sobolev mappings
Antoine Detaille, Jean Van Schaftingen
https://arxiv.org/abs/2506.16204 https://arxiv.org/pdf…

Heterotopic energy for Sobolev mappings
We study the notion of heterotopic energy defined as the limit of Sobolev energies of Sobolev mappings in a given homotopy class approximating almost everywhere a given Sobolev mapping. We show that the heterotopic energy is finite if and only if the mappings in the corresponding homotopy classes are homotopic on a codimension one skeleton of a triangulation of the domain. When this is the case, the heterotopic energy of a mapping is the sum of its Sobolev energy and its disparity energy, defin…

Sixteen years ago today, the #OpenStreetMap #Philippines community 🇵🇭 held the first ever mapping party in the country. And our target was the picturesque city of #Tagaytay.

Volumetric Parameterization for 3-Dimensional Simply-Connected Manifolds
Zhiyuan Lyu, Qiguang Chen, Gary P. T. Choi, Lok Ming Lui
https://arxiv.org/abs/2506.17025

Volumetric Parameterization for 3-Dimensional Simply-Connected Manifolds
With advances in technology, there has been growing interest in developing effective mapping methods for 3-dimensional objects in recent years. Volumetric parameterization for 3D solid manifolds plays an important role in processing 3D data. However, the conventional approaches cannot control the bijectivity and local geometric distortions of the result mappings due to the complex structure of the solid manifolds. Moreover, prior methods mainly focus on one property instead of balancing differe…

Project AMIGA: The Inner Circumgalactic Medium of Andromeda from Thick Disk to Halo
Nicolas Lehner, J. Christopher Howk, Lucy Collins, Sameer, Bart P. Wakker, Ramona Augustin, Kathleen A. Barger, Michelle A. Berg, Rongmon Bordoloi, Thomas M. Brown, Frances H. Cashman, Claude-Andr\'e Faucher-Gigu\`ere, Andrew J. Fox, David M. French, Karoline M. Gilbert, Puragra Guhathakurta, John M. O'Meara, Brian W. O'Shea, Molly S. Peeples, D. J. Pisano, J. Xavier Prochaska, Jonathan Ste…

Project AMIGA: The Inner Circumgalactic Medium of Andromeda from Thick Disk to Halo
The inner circumgalactic medium (CGM) of galaxies, where disk and halo processes intersect, remains poorly characterized despite its critical role in regulating galaxy evolution. We present results from Project AMIGA Insider, mapping Andromeda's (M31) inner CGM within 0.25 R_vir (~75 kpc) using 11 QSO sightlines, bringing our total sample to 54 sightlines from the disk to 2 R_vir. We detect a clear transition between M31's thick disk and CGM at R < 30 kpc, where low/intermediate ions show thick…

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Jiayue Melissa Shi, Dong Whi Yoo, Keran Wang, Violeta J. Rodriguez, Ravi Karkar, Koustuv Saha
https://arxiv.org/abs/2506.15047

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot …

Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning
Min Namgung, JangHyeon Lee, Fangyi Ding, Yao-Yi Chiang
https://arxiv.org/abs/2506.15113

Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning
Ensuring equitable public transit access remains challenging, particularly in densely populated cities like New York City (NYC), where low-income and minority communities often face limited transit accessibility. Bike-sharing systems (BSS) can bridge these equity gaps by providing affordable first- and last-mile connections. However, strategically expanding BSS into underserved neighborhoods is difficult due to uncertain bike-sharing demand at newly planned ("cold-start") station locations and …

Good Morning #Canada
In 1713, with the Treaty of Utrecht, Acadia was ceded to the British. On June 23rd of that year, a proclamation was issued that all Acadians must pledge allegiance to Britain or leave the territory. After decades of resistance, in 1755, the British began forcibly deporting Acadians. This proved the British applied equal treatment to anyone who lived in the eastern territory before them, Native or French.
#CanadaIsAwesome #History #Colonialism
https://canadiangeographic.ca/articles/mapping-the-acadian-deportations/

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Miguel \'A. Gonz\'alez-Santamarta, Francisco J. Rodr\'iguez-Lera, Vicente Matell\'an-Olivera
https://arxiv.org/abs/2506.09583

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Localization plays a crucial role in the navigation capabilities of autonomous robots, and while indoor environments can rely on wheel odometry and 2D LiDAR-based mapping, outdoor settings such as agriculture and forestry, present unique challenges that necessitate real-time localization and consistent mapping. Addressing this need, this paper introduces the VAULT prototype, a ROS 2-based mobile mapping system (MMS) that combines various sensors to enable robust outdoor and indoor localization.…

Software Security Mapping Framework: Operationalization of Security Requirements
Sung Une Lee, Liming Dong, Zhenchang Xing, Muhammad Ejaz Ahmed, Stefan Avgoustakis
https://arxiv.org/abs/2506.11051

Software Security Mapping Framework: Operationalization of Security Requirements
The escalating complexity of modern software development environments has heightened concerns around supply chain security. However, existing frameworks often fall short in translating abstract security principles into concrete, actionable practices. This paper introduces the Software Security Mapping Framework, a structured solution designed to operationalize security requirements across hierarchical levels -- from high-level regulatory standards (e.g., ISM, Australia cybersecurity standard pu…

Shimmering Darkness: Mapping the Evolution of Supernova-Neutrino-Boosted Dark Matter within the Milky Way
Yen-Hsun Lin, Meng-Ru Wu
https://arxiv.org/abs/2506.15151

Shimmering Darkness: Mapping the Evolution of Supernova-Neutrino-Boosted Dark Matter within the Milky Way
Supernova-neutrino-boosted dark matter (SN$ν$ BDM) has emerged as a promising portal for probing sub-GeV dark matter. In this work, we investigate the behavior of BDM signatures originating from core-collapse supernovae (CCSNe) within the Milky Way (MW) over the past one hundred thousand years, examining both their temporal evolution and present-day spatial distributions. We show that while the MW BDM signature is approximately diffuse in the nonrelativistic regime, it exhibits significant tem…

EDNet: A Distortion-Agnostic Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training
Doyeop Kwak, Youngjoon Jang, Seongyu Kim, Joon Son Chung
https://arxiv.org/abs/2506.16231

EDNet: A Distortion-Agnostic Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training
Speech signals in real-world environments are frequently affected by various distortions such as additive noise, reverberation, and bandwidth limitation, which may appear individually or in combination. Traditional speech enhancement methods typically rely on either masking, which focuses on suppressing non-speech components while preserving observable structure, or mapping, which seeks to recover clean speech through direct transformation of the input. Each approach offers strengths in specifi…

Mapping Neural Theories of Consciousness onto the Common Model of Cognition
Paul S. Rosenbloom, John E. Laird, Christian Lebiere, Andrea Stocco
https://arxiv.org/abs/2506.12224

Mapping Neural Theories of Consciousness onto the Common Model of Cognition
A beginning is made at mapping four neural theories of consciousness onto the Common Model of Cognition. This highlights how the four jointly depend on recurrent local modules plus a cognitive cycle operating on a global working memory with complex states, and reveals how an existing integrative view of consciousness from a neural perspective aligns with the Com-mon Model.

High-fidelity entanglement and coherent multi-qubit mapping in an atom array
Aruku Senoo, Alexander Baumg\"artner, Joanna W. Lis, Gaurav M. Vaidya, Zhongda Zeng, Giuliano Giudici, Hannes Pichler, Adam M. Kaufman
https://arxiv.org/abs/2506.13632

High-fidelity entanglement and coherent multi-qubit mapping in an atom array
Neutral atoms in optical tweezer arrays possess broad applicability for quantum information science, in computing, simulation, and metrology. Among atomic species, Ytterbium-171 is unique as it hosts multiple qubits, each of which is impactful for these distinct applications. Consequently, this atom is an ideal candidate to bridge multiple disciplines, which, more broadly, has been an increasingly effective strategy within the field of quantum science. Realizing the full potential of this syner…

Hey @… - I had a couple of mapping nerds (I include myself in that category) ask about how the hexagon electoral map is created. Is there an explanation anywhere? Loved it in https://www.

Where the election will be won and lost — or maybe left hanging
The electoral maths on Saturday seems simple — win 76 seats and you win government. But that's not all there is to it.

Simultaneous Charge Carrier Density Mapping of SiC Epilayers and Substrates with Terahertz Time-Domain Spectroscopy
Joshua Hennig, Jens Klier, Stefan Duran, Kuei-Shen Hsu, Jan Beyer, Christian Roeder, Franziska C. Beyer, Nadine Schueler, Nico Vieweg, Katja Dutzi, Georg von Freymann, Daniel Molter
https://arxiv.org/abs/2506.14945…

Simultaneous Charge Carrier Density Mapping of SiC Epilayers and Substrates with Terahertz Time-Domain Spectroscopy
With the growing demand for efficient power electronics, SiC-based devices are progressively becoming more relevant. In contrast to established methods such as the mercury capacitance-voltage technique, terahertz spectroscopy promises a contactless characterization. In this work, we simultaneously determine the charge carrier density of SiC epilayers and their substrates in a single measurement over a wide range of about 8x10^(15)$ cm^(-3) to 4x10^(18) cm^(-3) using time-domain spectroscopy in …

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Chuanchao Gao, Niraj Kumar, Arvind Easwaran
https://arxiv.org/abs/2506.12686

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies.…

Non-amenability of mapping class groups of infinite-type surfaces and graphs
Yusen Long
https://arxiv.org/abs/2506.06538 https://arxi…

Non-amenability of mapping class groups of infinite-type surfaces and graphs
This paper completely determines the non-amenability of the mapping class groups of infinite-type surfaces, the mapping class groups of locally finite infinite graphs of higher genus, and exhibits a class of mapping class groups of trees that are amenable.

Symplectic semi-characteristics
Hao Zhuang
https://arxiv.org/abs/2505.14496 https://arxiv.org/pdf/2505.14496

Symplectic semi-characteristics
We study the symplectic semi-characteristic of a 4n-dimensional closed symplectic manifold. First, we define the symplectic semi-characteristic using the mapping cone complex model of the primitive cohomology. Second, using a vector field with nondegenerate zero points, we prove a counting formula for the symplectic semi-characteristic. As corollaries, we obtain an Atiyah type vanishing property and the fact that the symplectic semi-characteristic is independent of the choices of symplectic for…

Feature Intensity Mapping: Polycyclic Aromatic Hydrocarbon Emission from All Galaxies Across Cosmic Time
Yun-Ting Cheng, Brandon S. Hensley, Thomas S. -Y. Lai
https://arxiv.org/abs/2506.13863

Feature Intensity Mapping: Polycyclic Aromatic Hydrocarbon Emission from All Galaxies Across Cosmic Time
Line intensity mapping (LIM) is an emerging technique for probing the aggregate emission of a spectral line from all sources, without requiring individual detections. Through the wavelength-redshift relation, one can map the line-of-sight evolution of the line emission that traces the underlying large-scale structure in a spectral-imaging survey. In this work, we present a new technique -- feature intensity mapping -- as an extension of the LIM formalism to map broad spectral features in 3D, ra…

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Shaoqing Lin, Chong Teng, Fei Li, Donghong Ji, Lizhen Qu, Zhuang Li
https://arxiv.org/abs/2506.15583

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Vision-Language Models (VLMs) now generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers originally designed for single-sentence caption-to-graph mapping. Current approaches typically merge sentence-level parsing outputs for discourse input, often missing phenomena like cross-sentence coreference, resulting in fragmented graphs and degraded downstream VLM task performance. To address this, we introduce a new task, Discourse-level text Scene Graph pars…

Enhancing semi-resolved CFD-DEM for dilute to dense particle-fluid systems: A point cloud based, two-step mapping strategy via coarse graining
Yuxiang Liu, Lu Jing, Xudong Fu, Huabin Shi
https://arxiv.org/abs/2506.09517

Enhancing semi-resolved CFD-DEM for dilute to dense particle-fluid systems: A point cloud based, two-step mapping strategy via coarse graining
Computational fluid dynamics and discrete element method (CFD-DEM) coupling is an efficient and powerful tool to simulate particle-fluid systems. However, current volume-averaged CFD-DEM relying on direct grid-based mapping between the fluid and particle phases can exhibit a strong dependence on the fluid grid resolution, becoming unstable as particles move across fluid grids, and can fail to capture pore fluid pressure effects in very dense granular systems. Here we propose a two-step mapping …

TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splatting
Mae Younes, Adnane Boukhayma
https://arxiv.org/abs/2506.13348 https://…

TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splatting
Gaussian Splatting have demonstrated remarkable novel view synthesis performance at high rendering frame rates. Optimization-based inverse rendering within complex capture scenarios remains however a challenging problem. A particular case is modelling complex surface light interactions for highly reflective scenes, which results in intricate high frequency specular radiance components. We hypothesize that such challenging settings can benefit from increased representation power. We hence propos…

Waymo applied for a NYC permit to test its cars with safety drivers and plans to start collecting mapping data with manually driven cars in Manhattan in July (Andrew J. Hawkins/The Verge)
https://www.theverge.com/news/689093/waymo-nyc-permit…

Waymo’s robotaxis are coming back to New York City
Waymo applied for a permit to test autonomous vehicles with safety drivers in New York City. And it hopes it can change the state law to allow it to operate fully driverless.

Defining the Game Producer: A Mapping of Key Characteristics and Differentiators of the Professional Behind Digital Game Production
Rafael C. Lopes, Danilo M. Ribeiro
https://arxiv.org/abs/2506.14409

Defining the Game Producer: A Mapping of Key Characteristics and Differentiators of the Professional Behind Digital Game Production
Introduction: As digital games grow in complexity, the role of the Game Producer becomes increasingly relevant for aligning creative, technical, and business dimensions. Objective: This study aimed to identify and map the main characteristics, skills, and competencies that define the Digital Game Producer profile. Methodology: A qualitative investigation was conducted with 11 semi-structured interviews, analyzed through Grounded Theory to build categories grounded in professional practice. Resu…

Active InSAR monitoring of building damage in Gaza during the Israel-Hamas War
Corey Scher, Jamon Van Den Hoek
https://arxiv.org/abs/2506.14730 https://

Active InSAR monitoring of building damage in Gaza during the Israel-Hamas War
Aerial bombardment of the Gaza Strip beginning October 7, 2023 is one of the most intense bombing campaigns of the twenty-first century, driving widespread urban damage. Characterizing damage over a geographically dynamic and protracted armed conflict requires active monitoring. Synthetic aperture radar (SAR) has precedence for mapping disaster-induced damage with bi-temporal methods but applications to active monitoring during sustained crises are limited. Using interferometric SAR data from S…

TACS-Graphs: Traversability-Aware Consistent Scene Graphs for Ground Robot Indoor Localization and Mapping
Jeewon Kim, Minho Oh, Hyun Myung
https://arxiv.org/abs/2506.14178

TACS-Graphs: Traversability-Aware Consistent Scene Graphs for Ground Robot Indoor Localization and Mapping
Scene graphs have emerged as a powerful tool for robots, providing a structured representation of spatial and semantic relationships for advanced task planning. Despite their potential, conventional 3D indoor scene graphs face critical limitations, particularly under- and over-segmentation of room layers in structurally complex environments. Under-segmentation misclassifies non-traversable areas as part of a room, often in open spaces, while over-segmentation fragments a single room into overla…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang, Alexandra F Dobrita, Yingfu Xu, Kanishkan Vadivel, Kevin Shidqi, Paul Detterer Mario Konijnenburg, Gert-Jan van Schaik, Manolis Sifalakis, Zaid Al-Ars, Amirreza Yousefzadeh
https://…

SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design optimized for executing edge AI SNN/ANN inference applications efficiently. To speed up the silicon tape-out and chip design for SENECA, an accurate emulator, SENSIM, was designed. While SENSIM supports direct mapping of SNNs on neuromorphic architectures, as the SNN and ANNs grow in size, achieving optimal mapping fo…

Differential Spectrum and Boomerang Spectrum of Some Power Mapping
Yuehui Cui, Jinquan Luo
https://arxiv.org/abs/2506.05738 https://a…

Differential Spectrum and Boomerang Spectrum of Some Power Mapping
Let $f(x)=x^{s(p^m-1)}$ be a power mapping over $\mathbb{F}_{p^n}$, where $n=2m$ and $\gcd(s,p^m+1)=t$. In \cite{kpm-1}, Hu et al. determined the differential spectrum and boomerang spectrum of the power function $f$, where $t=1$. So what happens if $t\geq1$? In this paper, we extend the result of \cite{kpm-1} from $t=1$ to general case. We use a different method than in \cite{kpm-1} to determine the differential spectrum and boomerang spectrum of $f$ by studying the number of rational points o…

Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity
Bilal Saleh Husain
https://arxiv.org/abs/2506.12685 https://…

Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their susceptibility to adversarial attacks, particularly jailbreaking, poses significant safety and ethical concerns. While numerous jailbreak methods exist, many suffer from computational expense, high token usage, or complex decoding schemes. Liu et al. (2024) introduced FlipAttack, a black-box method that achieves high attack success rates (ASR) through simple prompt manipulation. This paper investigates the underly…

Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system
Sanna Jarl, Jens Sj\"olund, Robert J. W. Frost, Anders Holst, Jonathan J. S. Scragg
https://arxiv.org/abs/2506.05999

Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system
Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on …

Mirror Descent Using the Tempesta Generalized Multi-parametric Logarithms
Andrzej Cichocki
https://arxiv.org/abs/2506.13984 https://a…

Mirror Descent Using the Tempesta Generalized Multi-parametric Logarithms
In this paper, we develop a wide class Mirror Descent (MD) algorithms, which play a key role in machine learning. For this purpose we formulated the constrained optimization problem, in which we exploits the Bregman divergence with the Tempesta multi-parametric deformation logarithm as a link function. This link function called also mirror function defines the mapping between the primal and dual spaces and is associated with a very-wide (in fact, theoretically infinite) class of generalized tra…

Cosmological Remapping for Efficient Generation of 21 cm Intensity Mapping Mocks
Rahima Mokeddem, Bruno B. Bizarria, Jiajun Zhang, W. S. Hip\'olito-Ricaldi, Carlos Alexandre Wuensche, Elcio Abdalla, Filipe B. Abdalla, Amilcar R. Queiroz, Thyrso Villela, Bin Wang, Chang Feng, Edmar C. Gurj\~ao, Alessandro Marins
https://arxiv…

Cosmological Remapping for Efficient Generation of 21 cm Intensity Mapping Mocks
We present a novel application of cosmological rescaling, or "remapping," to generate 21 cm intensity mapping mocks for different cosmologies. The remapping method allows for computationally efficient generation of N-body catalogs by rescaling existing simulations. In this work, we employ the remapping method to construct dark matter halo catalogs, starting from the Horizon Run 4 simulation with WMAP5 cosmology, and apply it to different target cosmologies, including WMAP7, Planck18 and Chevall…

Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding
Shuai Yuan, Shuang Chen, Tianwu Lin, Jie Wang, Peng Gong
https://arxiv.org/abs/2506.02574

Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding
Accurate remote sensing geographic mapping depends heavily on representative and timely sample data. However, rapid changes in land surface dynamics necessitate frequent updates, quickly rendering previously collected samples obsolete and imposing significant labor demands for continuous manual updates. In this study, we aim to address this problem by dynamic sample generation using existing single-date static labeled samples. We introduce TasGen, a two-stage automated framework to automaticall…

I'm interested in making my own visualizations of weather radar data to display patterns in a region of the US (covered by multiple stations) over longish periods of time (e.g., a month).
Think something like this year of US radar, but smaller & shorter in my case: https://www.youtube.com/watch?v=FPuDPxK88g0
I'm familiar with mapping and animation in Python/R, but not weather radar data. I've been looking for tutorials, but haven't been able to track any down yet. Pointers very much welcome

OxO2 -- A SSSOM mapping browser for logically sound crosswalks
Henriette Harmse, Haider Iqbal, Helen Parkinson, James McLaughlin
https://arxiv.org/abs/2506.04286

OxO2 -- A SSSOM mapping browser for logically sound crosswalks
EMBL-EBI created OxO to enable users to map between datasets that are annotated with different ontologies. Mappings identified by the first version of OxO were not necessarily logically sound, missed important provenance information such as author and reviewer, and could timeout or crash for certain requests. In this paper we introduce OxO2 to address these concerns. Provenance is addressed by implementing SSSOM, a mapping standard that defines provenance for mappings. SSSOM defines the conditi…

Electronic properties of multilayered Lieb, transition, and Kagome lattices
T. F. O. Lara, E. B. Barros, W. P. Lima, J. P. G. Nascimento, J. Milton Pereira Jr., T. A. S. Pereira, D. R. da Costa
https://arxiv.org/abs/2506.15023

Electronic properties of multilayered Lieb, transition, and Kagome lattices
Based on the interconvertibility feature shared between monolayer Lieb and Kagome lattices, which allows mapping transition lattice's stages between these two limits ($π/2 \leqθ\leq 2π/3$), in this work we extend the recently proposed one-control ($θ$) parameter tight-binding model for the case of a multilayer Lieb-Kagome system, by considering the two most-common stacks: AA and AB (Bernal). We systematically study the band transformations between the two lattices by adjusting the interlaye…

On $\theta$-extension of continuous mapping
Andrew Ryabikov
https://arxiv.org/abs/2506.02228 https://arxiv.org/pdf/2506.02228

On $θ$-extension of continuous mapping
We consider the problem of constructing a weakly-continuous mapping extending continuous mapping defined on a dense set of a topological space to the entire space. Theorem on necessary and sufficient conditions for the existence of such an extension are proved. The axioms of separation and compactness of spaces are not assumed.

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Sokea Sang, Leanghok Hour, Youngsun Han
https://arxiv.org/abs/2506.09323

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple Quantum Processing Units (QPUs). However, this approach introduces significant challenges due to costly inter-core operations between chips and quantum state transfers, which contribute to noise and quantum decoherence. This paper presents QARMA, a novel Qubit mapping using Attention-based deep Reinforcement learning (DRL) for Modular quantum Architectures, along with …

Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solving
Cuizhi Zhou, Kaien Zhu
https://arxiv.org/abs/2506.08622

Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solving
The solution of partial differential equations (PDES) on irregular domains has long been a subject of significant research interest. In this work, we present an approach utilizing physics-informed neural networks (PINNs) to achieve irregular-to-regular domain mapping. Thus we can use finite difference method and physics-informed convolutional neural networks to solve PDEs on rectangular grids instead of the original irregular boundary. Structured grids on irregular domains are obtained by inv…

Surface Geologist 🚨Job opening🚨 #NorthDakota Geological Survey #geology
Field mapping of glacial sediments or sedimentary rock, geologic hazards (landslide) mapping, interpreting aerial and satellite imagery, etc.

Employment Opportunities

A novel method to trace the dark matter density profile around supermassive black holes with AGN reverberation mapping
Mayank Sharma, Gonzalo Herrera, Nahum Arav, Shunsaku Horiuchi
https://arxiv.org/abs/2506.10122

A novel method to trace the dark matter density profile around supermassive black holes with AGN reverberation mapping
We propose a new method to determine the dark matter density profile in the vicinity of distant supermassive black holes (SMBH) using reverberation mapping measurements of active galactic nuclei (AGN). For some AGN, the mapping of multiple emission lines allows us to measure the enclosed mass within different radii from the central SMBH, which allows us to infer or constrain the dark matter density profile on sub-pc scales. We find that for five out of fourteen AGN in our identified sample, the…

Mapping NVD Records to Their VFCs: How Hard is it?
Huu Hung Nguyen, Duc Manh Tran, Yiran Cheng, Thanh Le-Cong, Hong Jin Kang, Ratnadira Widyasari, Shar Lwin Khin, Ouh Eng Lieh, Ting Zhang, David Lo
https://arxiv.org/abs/2506.09702

Mapping NVD Records to Their VFCs: How Hard is it?
Mapping National Vulnerability Database (NVD) records to vulnerability-fixing commits (VFCs) is crucial for vulnerability analysis but challenging due to sparse explicit links in NVD references.This study explores this mapping's feasibility through an empirical approach. Manual analysis of NVD references showed Git references enable over 86% success, while non-Git references achieve under 14%. Using these findings, we built an automated pipeline extracting 31,942 VFCs from 20,360 NVD records (8…

Replaced article(s) found for math.GT. https://arxiv.org/list/math.GT/new
[1/1]:
A four-dimensional mapping class group relation
https://

A Smooshed BMOCZ Zero Constellation for CFO Estimation Without Channel Coding
Anthony Joseph Perre, Parker Huggins, Alphan Sahin
https://arxiv.org/abs/2506.12599

A Smooshed BMOCZ Zero Constellation for CFO Estimation Without Channel Coding
In this study, we propose a new binary modulation on conjugate-reciprocal zeros (BMOCZ) zero constellation, which we call smooshed binary modulation on conjugate-reciprocal zeros (SBMOCZ), to address carrier frequency offset (CFO)-induced zero rotation without depending on channel coding. In our approach, we modify the phase mapping of Huffman BMOCZ by shrinking the angle between adjacent zeros, except for the first and last, to introduce a gap in the zero constellation. By discerning the gap l…

Uniform stability for the matrix inverse Sturm-Liouville problems
Natalia P. Bondarenko
https://arxiv.org/abs/2506.15300 https://arxi…

Uniform stability for the matrix inverse Sturm-Liouville problems
In this paper, the uniform stability of the inverse spectral problem is proved for the matrix Sturm-Liouville operator on a finite interval. Namely, we describe the sets of spectral data, on which the inverse spectral mapping is bounded and, consequently, the uniform estimates hold for the differences of the matrix potentials and of the corresponding coefficients of the boundary conditions. Our approach is based on a constructive procedure for solving the inverse problem by developing ideas of …

Prediction of steady states in a marine ecosystem model by a machine learning technique
Sarker Miraz Mahfuz, Thomas Slawig
https://arxiv.org/abs/2506.10475

Prediction of steady states in a marine ecosystem model by a machine learning technique
We used precomputed steady states obtained by a spin-up for a global marine ecosystem model as training data to build a mapping from the small number of biogeochemical model parameters onto the three-dimensional converged steady annual cycle. The mapping was performed by a conditional variational autoencoder (CVAE) with mass correction. Applied for test data, we show that the prediction obtained by the CVAE already gives a reasonable good approximation of the steady states obtained by a regular…

Mitigating antenna gain errors with HyFoReS in CHIME simulations
Haochen Wang, Panupong Phoompuang, Kiyoshi W. Masui, Arnab Chakraborty, Simon Foreman
https://arxiv.org/abs/2506.09170

Mitigating antenna gain errors with HyFoReS in CHIME simulations
Hybrid Foreground Residual Subtraction (HyFoReS) is a new family of algorithms designed to remove systematics-induced foreground contamination for 21-cm intensity mapping data. Previously, the algorithm was shown to be effective in mitigating beam perturbations in sky maps from the Canadian Hydrogen Intensity Mapping Experiment (CHIME). In this study, we apply HyFoReS to CHIME simulations and test the algorithm's ability to mitigate antenna gain-type systematics in polarized visibilities. Simul…

‘Charles Ignatius Sancho: A Black British Man of Letters and His World’
https://ift.tt/meCybfk
updated: Wednesday, June 18, 2025 - 10:49amfull name / name of organization: Mapping Black…
via Input 4 RELCFP

Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction
Ka Hung Chan, Xinyue Huang, Nobumichi Tamura, Xian Chen
https://arxiv.org/abs/2506.10937

Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction
Understanding the grain morphology, orientation distribution, and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapp…

Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Yunqi Yang, Karl Tayeb, Peter Carbonetto, Xiaoyuan Zhong, Carole Ober, Matthew Stephens
https://arxiv.org/abs/2506.06233

Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is designed to deal with the specific challenges that arise in genetic fine-mapping, including: the presence of very strong correlations among the covariates, often exceeding 0.99; very large data sets containing potentially thousands of covariates and hundreds of thousands of samples. We accomplish this by extending the…

High-fidelity collisional quantum gates with fermionic atoms
Petar Bojovi\'c, Timon Hilker, Si Wang, Johannes Obermeyer, Marnix Barendregt, Dorothee Tell, Thomas Chalopin, Philipp M. Preiss, Immanuel Bloch, Titus Franz
https://arxiv.org/abs/2506.14711

High-fidelity collisional quantum gates with fermionic atoms
Quantum simulations of electronic structure and strongly correlated quantum phases are widely regarded as among the most promising applications of quantum computing. These simulations require the accurate implementation of motion and entanglement of fermionic particles. Instead of the commonly applied costly mapping to qubits, fermionic quantum computers offer the prospect of directly implementing electronic structure problems. Ultracold neutral atoms have emerged as a powerful platform for spi…

Trust the process: mapping data-driven reconstructions to informed models using stochastic processes
Stefano Rinaldi, Alexandre Toubiana, Jonathan R. Gair
https://arxiv.org/abs/2506.05153

Trust the process: mapping data-driven reconstructions to informed models using stochastic processes
Gravitational-wave astronomy has entered a regime where it can extract information about the population properties of the observed binary black holes. The steep increase in the number of detections will offer deeper insights, but it will also significantly raise the computational cost of testing multiple models. To address this challenge, we propose a procedure that first performs a non-parametric (data-driven) reconstruction of the underlying distribution, and then remaps these results onto a …

I’ve been working on a fork of @…’s MeshInfo for PugetMesh. If you’re interested in #Meshtastic in the Puget Sound area, check it out. It shows chat messages, neighboring nodes, node details, and a mesh map.
I added mapping for message and traceroute propagatio…

Beyond the Hype: Mapping Uncertainty and Gratification in AI Assistant Use
Karen Joy, Tawfiq Ammari, Alyssa Sheehan
https://arxiv.org/abs/2506.09220 https:…

Beyond the Hype: Mapping Uncertainty and Gratification in AI Assistant Use
This paper examines the gap between the promises and real-world performance of emerging AI personal assistants. Drawing on interviews with early adopters of devices like Rabbit R1 and Humane AI Pin, as well as services like Ohai and Docus, we map user experiences through the lens of Uses and Gratifications and Uncertainty Reduction Theory. We identify three core types of user uncertainty, functional, interactional, and social, and explore how each disrupts different user gratifications. We show…

The Integral Decimation Method for Quantum Dynamics and Statistical Mechanics
Ryan T. Grimm, Alexander J. Staat, Joel D. Eaves
https://arxiv.org/abs/2506.11341

The Integral Decimation Method for Quantum Dynamics and Statistical Mechanics
We present a method to numerically evaluate functional integrals called integral decimation (ID). It constructs a separable decomposition of the integrand as a spectral tensor train (STT), a continuous generalization of the matrix product state. ID builds the STT by mapping the integrand to an auxiliary many-body wavefunction that evolves in time from an initially unentangled state. Each body-ordered term of the action corresponds to a quantum gate, applied to the state during its evolution. Th…

The relative index in coarse index theory and submanifold obstructions to uniform positive scalar curvature
Alexander Engel, Christopher Wulff
https://arxiv.org/abs/2506.14301

The relative index in coarse index theory and submanifold obstructions to uniform positive scalar curvature
We provide a coarse version of the relative index of Gromov and Lawson and thoroughly establish all of its basic properties. As an application, we discuss a general procedure to construct wrong way maps on the $K$-theory of the Roe algebra mapping the coarse index class of the Dirac operator of a manifold to the one of a suitably embedded submanifold of arbitrary codimension, thereby establishing an abstract machinery to find obstructions to uniform positive scalar curvature coming from these s…

A Mapping Study About Training in Industry Context in Software Engineering
Breno Alves de Andrade, Rodrigo Siqueira, Lidiane Gomes, Antonio Oliveira, Danilo Monteiro Ribeiro
https://arxiv.org/abs/2506.12590

A Mapping Study About Training in Industry Context in Software Engineering
Context: Corporate training plays a strategic role in the continuous development of professionals in the software engineering industry. However, there is a lack of systematized understanding of how training initiatives are designed, implemented, and evaluated within this domain. Objective: This study aims to map the current state of research on corporate training in software engineering in industry settings, using Eduardo Salas' training framework as an analytical lens. Method: A systematic…

Bedrock Ocean, which has developed an autonomous underwater vehicle to map the ocean floor using sonar and magnetic sensors, raised a $25M Series A-2 (Tim De Chant/TechCrunch)
https://techcrunch.com/2025/06/10/bedrock-ocean-dredges-u…

Exclusive: Bedrock Ocean dredges up $25M to map the seafloor with robots | TechCrunch
Bedrock Ocean wants to use autonomous underwater vehicles to make mapping the seafloor faster and cheaper.

First systematic experimental 2D mapping of linearly polarized $\gamma$-ray polarimetric distribution in relativistic Compton scattering
Kaijie Chen, Xiangfei Wang, Hanghua Xu, Gongtao Fan, Zirui Hao, Longxiang Liu, Yue Zhang, Sheng Jin, Zhicai Li, Pu Jiao, Qiankun Sun, Zhenwei Wang, Mengdie Zhou, Mengke Xu, Hongwei Wang, Wenqing Shen, Yugang Ma
http…

First systematic experimental 2D mapping of linearly polarized $γ$-ray polarimetric distribution in relativistic Compton scattering
The interaction of photons with relativistic electrons constitutes a fundamental electromagnetic process whose polarization transfer mechanics remain incompletely characterized. We report the first systematic measurement of spatial polarization distribution for $γ$-rays generated via \SI{45}{\degree} slant inverse Compton scattering (ICS) between linearly polarized \SI{0.117}{\eV} photons and \SI{3.5}{\GeV} electrons, performing full 2D mapping of intensity, polarization angle (AOP), and degre…

There are any number of ways to keep Altadena affordable.
Ban single-family detached dwellings.
Ban off-street parking.
Build 6-story affordable housing on scattered sites.
The aim here is to maintain the area's high social status via exclusion. Based on uncool-ness rather than income or race.
[reply to: https://

Towards Autonomous In-situ Soil Sampling and Mapping in Large-Scale Agricultural Environments
Thien Hoang Nguyen, Erik Muller, Michael Rubin, Xiaofei Wang, Fiorella Sibona, Salah Sukkarieh
https://arxiv.org/abs/2506.05653

Towards Autonomous In-situ Soil Sampling and Mapping in Large-Scale Agricultural Environments
Traditional soil sampling and analysis methods are labor-intensive, time-consuming, and limited in spatial resolution, making them unsuitable for large-scale precision agriculture. To address these limitations, we present a robotic solution for real-time sampling, analysis and mapping of key soil properties. Our system consists of two main sub-systems: a Sample Acquisition System (SAS) for precise, automated in-field soil sampling; and a Sample Analysis Lab (Lab) for real-time soil property ana…

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Min Li, Chen Chen, Zhenghao Li, Yin Liu, Shanshan Shan, Peng Wu, Pengfei Rong, Feng Liu, G. Bruce Pike, Alan H. Wilman, Hongfu Sun, Yang Gao
https://arxiv.org/abs/2506.13293

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Quantitative susceptibility mapping (QSM) provides a valuable tool for quantifying susceptibility distributions in human brains; however, two types of opposing susceptibility sources (i.e., paramagnetic and diamagnetic), may coexist in a single voxel, and cancel each other out in net QSM images. Susceptibility source separation techniques enable the extraction of sub-voxel information from QSM maps. This study proposes a novel SUSEP-Net for susceptibility source separation by training a dual-br…

On Universal Inverse Monoids with the $F$-Inverse Property
Igor Dolinka, Ganna Kudryavtseva
https://arxiv.org/abs/2506.14047 https://…

On Universal Inverse Monoids with the $F$-Inverse Property
An inverse monoid $M$ is called $F$-inverse if each $σ$-class of $M$, where $σ$ is the minimum group congruence of $M$, has a maximum element with respect to the natural order of $M$. Upon Kinyon's observation made in 2018 that $F$-inverse monoids form a variety in the enriched signature involving an additional unary operation mapping each element of $M$ to the maximum element of its $σ$-class, a separate theory of $F$-inverse monoids is developing in recent years, greatly aided by a recent …

This https://arxiv.org/abs/2410.08902 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Residual finiteness of some automorphism groups of high dimensional manifolds
We show that for a smooth, closed 2-connected manifold $M$ of dimension $d \geq 6$, the topological mapping class group $π_0 \mathrm{Homeo}(M)$ is residually finite, in contrast to the situation for the smooth mapping class group $π_0 \mathrm{Diff}(M)$. Combined with a result of Sullivan, this implies that $π_0 \mathrm{Homeo}(M)$ is an arithmetic group. The proof uses embedding calculus, and is of independent interest: we show that the $T_k$-mapping class group, $π_0 T_k \mathrm{Diff}(M)$, …

Echo mapping of the black hole accretion flow in NGC 7469
Raj Prince, Juan V. Hern\'andez Santisteban, Keith Horne, J. Gelbord, Ian McHardy, R. Edelson, C. A. Onken, F. R. Donnan, M. Vestergaard, S. Kaspi, H. Winkler, E. M. Cackett, H. Landt, A. J. Barth, T. Treu, S. Valenti, P. Lira, D. Chelouche, E. Romero Colmenero, M. R. Goad, D. H. Gonzalez-Buitrago, E. Kara, C. Villforth

Echo mapping of the black hole accretion flow in NGC 7469
Reverberation mapping (RM) can measure black hole accretion disc sizes and radial structure through observed time lags that should increase with wavelength as $τ\proptoλ^{4/3}$. Our 250-day RM campaign on NGC 7469 combines sub-day cadence 7-band photometry from the Las Cumbres Observatory robotic telescopes and weekly X-ray and UVOT data from Swift. By fitting these light curves, we measure the spectral energy distribution of the variable accretion disc and inter-band lags of just 1.5 days ac…

Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection
Nils Dengler, Jesper M\"ucke, Rohit Menon, Maren Bennewitz
https://arxiv.org/abs/2506.02286

Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection
Service robots operating in cluttered human environments such as homes, offices, and schools cannot rely on predefined object arrangements and must continuously update their semantic and spatial estimates while dealing with possible frequent rearrangements. Efficient and accurate mapping under such conditions demands selecting informative viewpoints and targeted manipulations to reduce occlusions and uncertainty. In this work, we present a manipulation-enhanced semantic mapping framework for oc…

Neural Functions for Learning Periodic Signal
Woojin Cho, Minju Jo, Kookjin Lee, Noseong Park
https://arxiv.org/abs/2506.09526 https://

Neural Functions for Learning Periodic Signal
As function approximators, deep neural networks have served as an effective tool to represent various signal types. Recent approaches utilize multi-layer perceptrons (MLPs) to learn a nonlinear mapping from a coordinate to its corresponding signal, facilitating the learning of continuous neural representations from discrete data points. Despite notable successes in learning diverse signal types, coordinate-based MLPs often face issues of overfitting and limited generalizability beyond the train…

What Comes After Harm? Mapping Reparative Actions in AI through Justice Frameworks
Sijia Xiao, Haodi Zou, Alice Qian Zhang, Deepak Kumar, Hong Shen, Jason Hong, Motahhare Eslami
https://arxiv.org/abs/2506.05687

What Comes After Harm? Mapping Reparative Actions in AI through Justice Frameworks
As Artificial Intelligence (AI) systems are integrated into more aspects of society, they offer new capabilities but also cause a range of harms that are drawing increasing scrutiny. A large body of work in the Responsible AI community has focused on identifying and auditing these harms. However, much less is understood about what happens after harm occurs: what constitutes reparation, who initiates it, and how effective these reparations are. In this paper, we develop a taxonomy of AI harm rep…

Volumetric Functional Maps
Filippo Maggioli, Marco Livesu, Simone Melzi
https://arxiv.org/abs/2506.13212 https://arxiv.org/pdf/2506.1…

Volumetric Functional Maps
The computation of volumetric correspondences between 3D shapes has great potential for medical and industrial applications. In this work, we pave the way for spectral volume mapping, extending for the first time the functional maps framework from the surface setting to the volumetric domain. We show that the eigenfunctions of the volumetric Laplace operator define a functional space that is suitable for high-quality signal transfer. We also experiment with various techniques that edit this fun…

Extremal Magic States from Symmetric Lattices
Misaki Ohta, Kazuki Sakurai
https://arxiv.org/abs/2506.11725 https://arxiv.org/pdf/2506…

Extremal Magic States from Symmetric Lattices
Magic, a key quantum resource beyond entanglement, remains poorly understood in terms of its structure and classification. In this paper, we demonstrate a striking connection between high-dimensional symmetric lattices and quantum magic states. By mapping vectors from the $E_8$, $BW_{16}$, and $E_6$ lattices into Hilbert space, we construct and classify stabiliser and maximal magic states for two-qubit, three-qubit and one-qutrit systems. In particular, this geometric approach allows us to cons…

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
Guang-Xing Li
https://arxiv.org/abs/2506.05758 …

Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
The development of science has been transforming man's view towards nature for centuries. Observing structures and patterns in an effective approach to discover regularities from data is a key step toward theory-building. With increasingly complex data being obtained, revealing regularities systematically has become a challenge. Correlation is a most commonly-used and effective approach to describe regularities in data, yet for complex patterns, spatial inhomogeneity and complexity can often un…

Towards Scalable Schema Mapping using Large Language Models
Christopher Buss, Mahdis Safari, Arash Termehchy, Stefan Lee, David Maier
https://arxiv.org/abs/2505.24716

Towards Scalable Schema Mapping using Large Language Models
The growing need to integrate information from a large number of diverse sources poses significant scalability challenges for data integration systems. These systems often rely on manually written schema mappings, which are complex, source-specific, and costly to maintain as sources evolve. While recent advances suggest that large language models (LLMs) can assist in automating schema matching by leveraging both structural and natural language cues, key challenges remain. In this paper, we iden…

Haptic-Based User Authentication for Tele-robotic System
Rongyu Yu, Kan Chen, Zeyu Deng, Chen Wang, Burak Kizilkaya, Liying Emma Li
https://arxiv.org/abs/2506.14116

Haptic-Based User Authentication for Tele-robotic System
Tele-operated robots rely on real-time user behavior mapping for remote tasks, but ensuring secure authentication remains a challenge. Traditional methods, such as passwords and static biometrics, are vulnerable to spoofing and replay attacks, particularly in high-stakes, continuous interactions. This paper presents a novel anti-spoofing and anti-replay authentication approach that leverages distinctive user behavioral features extracted from haptic feedback during human-robot interactions. To …

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
Vahan Arsenyan, Elen Vardanyan, Arnak Dalalyan
https://arxiv.org/abs/2506.09681

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
Generative modeling aims to produce new random examples from an unknown target distribution, given access to a finite collection of examples. Among the leading approaches, denoising diffusion probabilistic models (DDPMs) construct such examples by mapping a Brownian motion via a diffusion process driven by an estimated score function. In this work, we first provide empirical evidence that DDPMs are robust to constant-variance noise in the score evaluations. We then establish finite-sample guara…

Unveiling the small-scale web around galaxies with miniJPAS and DESI: the role of local connectivity in star formation
Daniela Gal\'arraga-Espinosa, Guinevere Kauffmann, Silvia Bonoli, Luisa Lucie-Smith, Rosa M. Gonz\'alez Delgado, Elmo Tempel, Raul Abramo, Siddharta Gurung-L\'opez, Valerio Marra, Jailson Alcaniz, Narciso Benitez, Saulo Carneiro, Javier Cenarro, David Crist\'obal-Hornillos, Renato Dupke, Alessandro Ederoclite, Antonio Hern\'an-Caballero, Carlos Hern…

Unveiling the small-scale web around galaxies with miniJPAS and DESI: the role of local connectivity in star formation
We present the first statistical observational study detecting small-scale filaments in the immediate surroundings of galaxies, i.e. the local web of galaxies. Simulations predict that cold gas, the fuel for star formation, is channeled through filamentary structures into galaxies. Yet, direct observational evidence for this process has been limited by the challenge of mapping the cosmic web at small scales. Using miniJPAS spectro-photometric data combined with spectroscopic DESI redshifts when…

DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
Jiajun Jiang, Yiming Zhu, Zirui Wu, Jie Song
https://arxiv.org/abs/2506.01950

DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
We introduce DualMap, an online open-vocabulary mapping system that enables robots to understand and navigate dynamically changing environments through natural language queries. Designed for efficient semantic mapping and adaptability to changing environments, DualMap meets the essential requirements for real-world robot navigation applications. Our proposed hybrid segmentation frontend and object-level status check eliminate the costly 3D object merging required by prior methods, enabling effi…

Flying Co-Stereo: Enabling Long-Range Aerial Dense Mapping via Collaborative Stereo Vision of Dynamic-Baseline
Zhaoying Wang, Xingxing Zuo, Wei Dong
https://arxiv.org/abs/2506.00546

Flying Co-Stereo: Enabling Long-Range Aerial Dense Mapping via Collaborative Stereo Vision of Dynamic-Baseline
Lightweight long-range mapping is critical for safe navigation of UAV swarms in large-scale unknown environments. Traditional stereo vision systems with fixed short baselines face limited perception ranges. To address this, we propose Flying Co-Stereo, a cross-agent collaborative stereo vision system that leverages the wide-baseline spatial configuration of two UAVs for long-range dense mapping. Key innovations include: (1) a dual-spectrum visual-inertial-ranging estimator for robust baseline e…

Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps
Zhanxin Hao, Jie Cao, Ruimiao Li, Jifan Yu, Zhiyuan Liu, Yu Zhang
https://arxiv.org/abs/2506.02993

Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps
Multi-agent AI systems, which simulate diverse instructional roles such as teachers and peers, offer new possibilities for personalized and interactive learning. Yet, student-AI interaction patterns and their pedagogical implications remain unclear. This study explores how university students engaged with multiple AI agents, and how these interactions influenced cognitive outcomes (learning gains) and non-cognitive factors (motivation, technology acceptance). Based on MAIC, an online learning p…

Mapping the Spatial Distribution of Fast Radio Bursts within their Host Galaxies
Alexa C. Gordon (Northwestern), Wen-fai Fong, Adam T. Deller, Lachlan Marnoch, Sungsoon Lim, Eric W. Peng, Keith W. Bannister, Apurba Bera, N. D. R. Bhat, Tyson Dial, Yuxin Dong, Tarraneh Eftekhari, Marcin Glowacki, Kelly Gourdji, Vivek Gupta, Joscha N. Jahns-Schindler, Akhil Jaini, Charles D. Kilpatrick, Chang Liu, J. Xavier Prochaska, Stuart D. Ryder, Ryan M. Shannon, Sunil Simha, Nicolas Tejos, Yuanming…

Mapping the Spatial Distribution of Fast Radio Bursts within their Host Galaxies
We present deep optical and near-infrared observations of the host galaxies of 34 fast radio bursts (FRBs) detected by the Commensal Real-time ASKAP Fast Transient (CRAFT) survey on the Australian SKA Pathfinder (ASKAP) to compare the locations of FRBs relative to their host light distributions. Incorporating three additional FRBs from the literature, for a total of four repeating and 33 apparently non-repeating FRBs, we determine their projected galactocentric offsets and find a median of $ 4.…

Reverberation responses in light curves of the SBS1520 530 quasar
Liudmyla Berdina, Victoria Tsvetkova
https://arxiv.org/abs/2506.14222 https://

Reverberation responses in light curves of the SBS1520+530 quasar
Purpose: We aim to investigate an accretion disk (AD) temperature profile of a doubly lensed quasar SBS 1520+530. Temperature profiles of accretion disks are known to be diagnostic of many of the physical properties of AGNs and quasars. Methods: Our approach involves application of the photometric reverberation mapping to the light curves of SBS 1520+530 obtained in the Johnson-Cousins V and R filters. The RM method implies that the time shift between the light curves taken in different spectra…

Non-Abelian dynamics on a cube: improving quantum compilation through qudit-based simulations
Jacky Jiang, Natalie Klco, Olivia Di Matteo
https://arxiv.org/abs/2506.10945

Non-Abelian dynamics on a cube: improving quantum compilation through qudit-based simulations
Recent developments in mapping lattice gauge theories relevant to the Standard Model onto digital quantum computers identify scalable paths with well-defined quantum compilation challenges toward the continuum. As an entry point to these challenges, we address the simulation of SU(2) lattice gauge theory. Using qudit registers to encode the digitized gauge field, we provide quantum resource estimates, in terms of elementary qudit gates, for arbitrarily high local gauge field truncations. We the…

Barrier Method for Inequality Constrained Factor Graph Optimization with Application to Model Predictive Control
Anas Abdelkarim, Holger Voos, Daniel G\"orges
https://arxiv.org/abs/2506.14341

Barrier Method for Inequality Constrained Factor Graph Optimization with Application to Model Predictive Control
Factor graphs have demonstrated remarkable efficiency for robotic perception tasks, particularly in localization and mapping applications. However, their application to optimal control problems -- especially Model Predictive Control (MPC) -- has remained limited due to fundamental challenges in constraint handling. This paper presents a novel integration of the Barrier Interior Point Method (BIPM) with factor graphs, implemented as an open-source extension to the widely adopted g2o framework. O…

A Systematic Mapping Study on Software Architecture for AI-based Mobility Systems
Amra Ramic, Stefan Kugele
https://arxiv.org/abs/2506.01595 https://

A Systematic Mapping Study on Software Architecture for AI-based Mobility Systems
Background: Due to their diversity, complexity, and above all importance, safety-critical and dependable systems must be developed with special diligence. Criticality increases as these systems likely contain artificial intelligence (AI) components known for their uncertainty. As software and reference architectures form the backbone of any successful system, including safety-critical dependable systems with learning-enabled components, choosing the suitable architecture that guarantees safety …

Invariant Extended Kalman Filter for Autonomous Surface Vessels with Partial Orientation Measurements
Derek Benham, Easton Potokar, Joshua G. Mangelson
https://arxiv.org/abs/2506.10850

Invariant Extended Kalman Filter for Autonomous Surface Vessels with Partial Orientation Measurements
Autonomous surface vessels (ASVs) are increasingly vital for marine science, offering robust platforms for underwater mapping and inspection. Accurate state estimation, particularly of vehicle pose, is paramount for precise seafloor mapping, as even small surface deviations can have significant consequences when sensing the seafloor below. To address this challenge, we propose an Invariant Extended Kalman Filter (InEKF) framework designed to integrate partial orientation measurements. While con…

Scatter in the star formation rate-halo mass relation: secondary bias and its impact on line-intensity mapping
Rui Lan Jun (UTokyo), Tom Theuns (ICC, Durham), Kana Moriwaki (UTokyo), Sownak Bose (ICC, Durham)
https://arxiv.org/abs/2506.03015

Scatter in the star formation rate-halo mass relation: secondary bias and its impact on line-intensity mapping
We use the IllustrisTNG cosmological hydrodynamical simulations to study the impact of secondary bias -- specifically, the correlation between star formation rate (SFR) and halo bias at fixed halo mass -- on the line-intensity mapping (LIM) power spectrum. In LIM, the galaxy contributions are flux-weighted, and for many emission lines (e.g., H$α$), flux scales with SFR. We show that the (ensemble-averaged) large-scale two-halo term of the power spectrum depends only on the mean luminosity-halo…

This https://arxiv.org/abs/2504.13070 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A quadratic estimator view of the transfer function correction in intensity mapping surveys
In single dish neutral hydrogen (HI) intensity mapping, signal separation methods such as principal component analysis (PCA) are used to clean the astrophysical foregrounds. PCA induces a signal loss in the estimated power spectrum, which can be corrected by a transfer function (TF). By injecting mock signals of HI into the data and performing the PCA cleaning, we can use the cleaned mock HI signal to cross-correlate with the original mock, and estimate the signal loss as a TF, ${T}(\vec{k})$. …

This https://arxiv.org/abs/2505.10018 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

LEMON-Mapping: Loop-Enhanced Large-Scale Multi-Session Point Cloud Merging and Optimization for Globally Consistent Mapping
Multi-robot collaboration is becoming increasingly critical and presents significant challenges in modern robotics, especially for building a globally consistent, accurate map. Traditional multi-robot pose graph optimization (PGO) methods ensure basic global consistency but ignore the geometric structure of the map, and only use loop closures as constraints between pose nodes, leading to divergence and blurring in overlapping regions. To address this issue, we propose LEMON-Mapping, a loop-enha…

This https://arxiv.org/abs/2506.01950 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
We introduce DualMap, an online open-vocabulary mapping system that enables robots to understand and navigate dynamically changing environments through natural language queries. Designed for efficient semantic mapping and adaptability to changing environments, DualMap meets the essential requirements for real-world robot navigation applications. Our proposed hybrid segmentation frontend and object-level status check eliminate the costly 3D object merging required by prior methods, enabling effi…

Discovery of an HI 21 cm absorption system at z=2.327 with CHIME
CHIME Collaboration, Mandana Amiri, Arnab Chakraborty, Simon Foreman, Mark Halpern, Alex S. Hill, Gary Hinshaw, Carolin Hofer, Albin Joseph, Joshua MacEachern, Kiyoshi W. Masui, Juan Mena-Parra, Arash Mirhosseini, Ue-Li Pen, Tristan Pinsonneault-Marotte, Alex Reda, J. Richard Shaw, Seth R. Siegel, Yukari Uchibori, Rik van Lieshout, Haochen Wang, Dallas Wulf

Discovery of an HI 21 cm absorption system at z=2.327 with CHIME
We report the detection of a new HI absorption system associated with the radio source NVSS J164725+375218 at a redshift of z=2.327, identified through a blind survey conducted by the Canadian Hydrogen Intensity Mapping Experiment (CHIME). This marks the fifth detection of an associated system at z > 2. By analyzing a subset of available data, we conducted a blind survey for HI absorption systems within the redshift range of 0.78 to 2.55 along 202 lines of sight in the declination range of 35 t…

Ultra-Slow-Roll Inflation on the Lattice II: Nonperturbative Curvature Perturbation
Angelo Caravano, Gabriele Franciolini, S\'ebastien Renaux-Petel
https://arxiv.org/abs/2506.11795

Ultra-Slow-Roll Inflation on the Lattice II: Nonperturbative Curvature Perturbation
Building on the recent lattice simulations of ultra-slow-roll (USR) dynamics presented in arXiv:2410.23942, we investigate the role of the nonlinear relation between the inflaton field configuration and the curvature perturbation $ζ$, the key observable after inflation. Using a nonperturbative $δN$ approach applied to the lattice output, we generate fully nonlinear three-dimensional maps of $ζ$. This calculation captures both the non-Gaussianity arising from the nonlinear mapping between $ϕ…

Mapping the reddening plane in the Galactic disk through interstellar extinction of open clusters
Yogesh C. Joshi (ARIES)
https://arxiv.org/abs/2506.04460 …

Mapping the reddening plane in the Galactic disk through interstellar extinction of open clusters
As thousands of new open clusters in the Galaxy have recently been reported with reddening or extinction information, we map the distribution and properties of the Galaxy's interstellar material in the Galactic disk as traced by these open clusters. By analyzing the distribution of interstellar extinction for 6215 open clusters located at low Galactic latitude b <= 6 deg, corresponding to the thin Galactic disk, we identify a reddening plane characterized by a dust layer whose thickness varies …

This https://arxiv.org/abs/2309.03678 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system ba…

Improving Long-Range Navigation with Spatially-Enhanced Recurrent Memory via End-to-End Reinforcement Learning
Fan Yang, Per Frivik, David Hoeller, Chen Wang, Cesar Cadena, Marco Hutter
https://arxiv.org/abs/2506.05997

Improving Long-Range Navigation with Spatially-Enhanced Recurrent Memory via End-to-End Reinforcement Learning
Recent advancements in robot navigation, especially with end-to-end learning approaches like reinforcement learning (RL), have shown remarkable efficiency and effectiveness. Yet, successful navigation still relies on two key capabilities: mapping and planning, whether explicit or implicit. Classical approaches use explicit mapping pipelines to register ego-centric observations into a coherent map frame for the planner. In contrast, end-to-end learning achieves this implicitly, often through rec…

This https://arxiv.org/abs/2409.16967 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning
Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative Path Planning (MRIPP) problems. In this paper, we propose a deep reinforcement learning approach for the MRIPP problem. We aim to maximize the number of discovered stationary targets in an unknown 3D …

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Haochen Liu, Tianyu Li, Haohan Yang, Li Chen, Caojun Wang, Ke Guo, Haochen Tian, Hongchen Li, Hongyang Li, Chen Lv
https://arxiv.org/abs/2506.09800

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, existing imitation learning (IL)-based models suffer from generalization to hard cases, and a lack of corrective feedback loop under post-deployment. While reinforcement learning (RL) offers a potential solution to tackle hard cases with optimality, it is often hindered by overfitting to specific driving cases, resulting i…